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Table of Contents

  1. General Policies
    1. Grading
      1. Final Exam
      2. Groups
      3. Assignments
    2. Readings
  2. Other Resources
    1. Friday Sections
    2. HowTos
  3. Topics
    1. Multiple Equation Models
    2. Some Non-parametrics
    3. Causality & Correlation
    4. Identification & Instrumental Variables
    5. Generalized Method of Moments
    6. Discrete Choice & Maximum Likelihood
    7. Resampling & the Bootstrap
    8. Cross-Validation & Other Ideas from Machine Learning

For class-related conversations, please go to edstem.org.

General Policies

Discussion of administrative issues: [video]

Grading

Final Exam

The final exam will be “take-home”, and be made available by 11am on Tuesday, May 9 (if there is a consensus that an earlier date is preferable then we can reschedule). You will have 24 hours to complete the final.

Groups

You should regard yourself as a member of a group, and your group will be responsible for working together on certain assignments. Group sizes should be between 3–6 (inclusive).

Assignments

We will ask you to complete an assignment every 2–3 weeks. We expect you to work on these with others in your group. Your group should turn in one set of notes and proposed solutions. The problem sets will feature two different kinds of problems.

  1. Exercises

    These are called exercises because they are meant more as tests of comprehension than as more difficult problems. Exercises will be graded on a “check”, “check +”, “check -” system.

    Not completing an exercise will be much worse for the grades of people in your group than receiving a “check -”.

  2. Problems

    Problems are meant less as checks on comprehension and more the part of the assignment that involves more critical thinking. “Problems” may not have a “right” answer (though there will always be many wrong answers).

Readings

  • “Hansen” refers to Bruce Hansen’s Econometrics Textbook. This was recently published by Princeton University Press, and belongs on your bookshelf.
  • Other readings will be added topic by topic (usually these will be available electronically).

Other Resources

Friday Sections

Lucy Hackett will hold weekly sections on Fridays. She’ll present material pertaining to both econometrics and programming.

It’s critical to note that we expect you to develop some fluency in the programming language python.

HowTos

Topics

Discussion of topics for 2nd half of course: [video]

Multiple Equation Models

Some Non-parametrics

Causality & Correlation

Identification & Instrumental Variables

Generalized Method of Moments

Discrete Choice & Maximum Likelihood

Resampling & the Bootstrap

Cross-Validation & Other Ideas from Machine Learning

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Teaching materials for ARE212.

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